Introduction

Very useful

You can access the course materials quickly from

https://ayoubbagheri.nl/r_tm/

Some guidelines

1- Please keep your microphone off

2- If you have a question, raise your hand or type your question in the chat

3- You may always interrupt me

4- We will introduce frequent question breaks

Lecturers and Assistants

José de Kruif

Dong Nguyen

Qixiang Fang

Kevin Patyk

Program

Time	Monday	Tuesday	Wednesday
9:00 - 10:30	Lecture 1	Lecture 3	Lecture 5
	Break	Break	Break
10:45 – 11:45	Practical 1	Practical 3	Practical 5
11:45 – 12:30	Discussion 1	Discussion 3	Discussion 5
	Lunch	Lunch	Lunch
13:45 – 15:15	Lecture 2	Lecture 4	Lecture 6
	Break	Break	Break
15:30 – 16:30	Practical 2	Practical 4	Practical 6
16:30 – 17:00	Discussion 2	Discussion 4	Discussion 6

Goal of the course

Text data is everywhere!
A lot of world’s data is in unstructured text format
The course teaches
- text mining techniques
- using R
- on a variety of applications
- in many domains.

What is text mining?

Text mining in an example

This is Garry!
Garry works at Bol.com (a webshop in the Netherlands)
He works in the dep of Customer relationship management.
He uses Excel to read and search customers’ reviews, extract aspects they wrote their reviews on, and identify their sentiments.
Curious about his job? See two examples!

This is a nice book for both young and old. It gives beautiful life lessons in a fun way. Definitely worth the money!

+ Educational

+ Funny

+ Price

Nice story for older children.

+ Funny

- Readability

Example

Garry likes his job a lot, but sometimes it is frustrating!
This is mainly because their company is expanding quickly!
Garry decides to hire Larry as his assistant.

Example

Still, a lot to do for two people!
Garry has some budget left to hire another assistant for couple of years!
He decides to hire Harry too!
Still, manual labeling using Excel is labor-intensive!

Challenges?

Can you guess what are the challenges Garry, Larry, and Harry encounter in doing their job, when working with text data?
- Go to www.menti.com and use the code 22 07 62 0

Challenges with text data

Huge amount of data
High dimensional but sparse
- all possible word and phrase types in the language!!

Challenges with text data

Ambiguity

Challenges with text data

Noisy data
- Examples: Abbreviations, spelling errors, short text
Complex relationships between words
- “Hema merges with Intertoys”
- “Intertoys is bought by Hema”

Example

During one of the coffee moments at the company, Garry was talking about their situation at the dep of Customer relationship management.
When Carrie, her colleague from the Data Science department, hears the situation, she offers Garry to use Text Mining!!
She says: “ Text mining is your friend; it can help you to make the process way faster than Excel by filtering words and recommending labels.”
She continues : “Text mining is a subfield of AI and NLP and is related to data science, data mining and machine learning.”
After consulting with Larry and Harry, they decide to give text mining a try!

Example

Text mining definition?

Which can be a part of Text Mining definition?
- The discovery by computer of new, previously unknown information from textual data
- Automatically extracting information from text
- Text mining is about looking for patterns in text
- Text mining describes a set of techniques that model and structure the information content of textual sources

(You can choose multiple answers)

Go to www.menti.com and use the code 22 07 62 0

Text mining definition

“the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources” Hearst (1999)
Text mining is about looking for patterns in text, in a similar way that data mining can be loosely described as looking for patterns in data.
Text mining describes a set of linguistic, statistical, and machine learning techniques that model and structure the information content of textual sources. (Wikipedia)

Another TM definition

Language is hard

Different things can mean more or less the same (“data science” vs. “statistics”)
Context dependency (“You have very nice shoes”);
Same words with different meanings (“to sanction”, “bank”);
Lexical ambiguity (“we saw her duck”)
Irony, sarcasm (“That’s just what I needed today!”, “Great!”, “Well, what a surprise.”)
Figurative language (“He has a heart of stone”)
Negation (“not good” vs. “good”), spelling variations, jargon, abbreviations
All the above are different over languages, 99% of work is on English!

Language is hard

We won’t solve linguistics …
In spite of the problems, text mining can be quite effective!

Examples & Applications

Text mining applications

Who wrote the Wilhelmus?

https://dh2017.adho.org/abstracts/079/079.pdf

Text Classification

Which ICD-10 codes should I give this doctor’s note?

Bovengenoemde patiënt was opgenomen op op de voor het specialisme Cardiologie.

Cardiovasculaire risicofactoren: Roken(-) Diabetes(-) Hypertensie(?) Hypercholesterolemie (?)

Anamnese. Om 18.30 pijn op de borst met uitstraling naar de linkerarm, zweten, misselijk. Ambulance gebeld en bij aansluiten monitor beeld van acuut onderwandinfarct. AMBU overdracht:.500mg aspegic iv, ticagrelor 180mg oraal, heparine, zofran eenmalig, 3x NTG spray. HD stabiel gebleven. . .Medicatie bij presentatie.Geen..

Lichamelijk onderzoek. Grauw, vegetatief, Halsvenen niet gestuwd. Cor s1 s2 geen souffles.Pulm schoon. Extr warm en slank .

Aanvullend onderzoek. AMBU ECG: Sinusritme, STEMI inferior III)II C/vermoedelijk RCA. Coronair angiografie. (…) .Conclusie angio: 1-vatslijden..PCI

Conclusie en beleid Bovengenoemde jarige man, blanco cardiale voorgeschiedenis, werd gepresenteerd vanwege een STEMI inferior waarvoor een spoed PCI werd verricht van de mid-RCA. Er bestaan geen relevante nevenletsels. Hij kon na de procedure worden overgeplaatst naar de CCU van het . ..Dank voor de snelle overname. ..Medicatie bij overplaatsing. Acetylsalicylzuur dispertablet 80mg ; oraal; 1 x per dag 80 milligram ; .Ticagrelor tablet 90mg ; oraal; 2 x per dag 90 milligram ; .Metoprolol tablet 50mg ; oraal; 2 x per dag 25 milligram ; .Atorvastatine tablet 40mg (als ca-zout-3-water) ; oraal; 1 x per dag 40 milligram ; Samenvatting Hoofddiagnose: STEMI inferior wv PCI RCA. Geen nevenletsels. Nevendiagnoses: geen. Complicaties: geen Ontslag naar: CCU .

Which ICD-10 codes should I give this doctor’s note?

Sentiment Analysis / Opinion Mining

Statistical Machine Translation

Dialog Systems

Question Answering

Go beyond search

Which studies go in my systematic review?

https://asreview.nl/

And more …

Automatically classify political news from sports news
Authorship identification
Age/gender identification
Language Identification
…

Process & Tasks

Text mining process

Text mining tasks

Text classification
Text clustering
Sentiment analysis
Feature selection
Topic modelling
Word embedding
Deep learning models
Responsible text mining
Text summarization

And more in NLP

source: https://nlp.stanford.edu/~wcmac/papers/20140716-UNLU.pdf

Regular expressions

Regular Expressions

In computing, a regular expression, also referred to as “regex” or “regexp”, provides a concise and flexible means for matching strings of text, such as particular characters, words, or patterns of characters. A regular expression is written in a formal language that can be interpreted by a regular expression processor.

http://en.wikipedia.org/wiki/Regular_expression

Regular Expressions

Really clever “wild card” expressions for matching and parsing strings.

http://en.wikipedia.org/wiki/Regular_expression

Understanding Regular Expressions

Very powerful and quite cryptic
Fun once you understand them
Regular expressions are a language unto themselves
A language of “marker characters” - programming with characters
It is kind of an “old school” language - compact

Regular expressions

A formal language for specifying text strings
How can we search for any of these?
- woodchuck
- woodchucks
- Woodchuck
- Woodchucks

Regular Expressions: Disjunctions

Letters inside square brackets
Ranges [A-Z]

Regular Expressions: Negation in Disjunction

Negations [^Ss]
- Carat means negation only when first in

Regular Expressions: More Disjunction

Woodchucks is another name for groundhog!
The pipe | for disjunction

Regular Expressions: ? * + .

Regular Expressions: Anchors ^ $

Example

Find me all instances of the word “the” in a text.

the

Misses capitalized examples

[tT]he

Incorrectly returns other or theology

[^a-zA-Z] [tT]he [^a-zA-Z]

Errors

The process we just went through was based on fixing two kinds of errors
- Matching strings that we should not have matched ( there, then, o ther)
  - False positives (Type I)
- Not matching things that we should have matched (The)
  - False negatives (Type II)

Errors cont.

In NLP we are always dealing with these kinds of errors.
Reducing the error rate for an application often involves two antagonistic efforts:
- Increasing accuracy or precision (minimizing false positives)
- Increasing coverage or recall (minimizing false negatives).

Regular Expression Quick Guide

Summary

Text data is everywhere!
Language is hard!
Sophisticated sequences of regular expressions are often the first model for any text processing tool
Regular expressions are a cryptic but powerful language for matching strings and extracting elements from those strings
The basic problem of text mining is that text is not a neat data set
One solution: text pre-processing

Practical 1

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